[PDF] Top 20 The asymptotic normality of internal estimator for nonparametric regression
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The asymptotic normality of internal estimator for nonparametric regression
... the regression function are widely used in various situations because of their flexibility and efficiency in the dependent and independent ...popular nonparametric estimator of the unknown function m(x) ... See full document
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Asymptotic properties of wavelet based estimator in nonparametric regression model with weakly dependent processes
... and asymptotic normality of ρ-mixing and ϕ-mixing dependent ...the nonparametric regression model with repeated observations under the specific ρ-mixing and ϕ -mixing dependent ...wavelet ... See full document
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Uniformly asymptotic normality of sample quantiles estimator for linearly negative quadrant dependent samples
... The concept of LNQD sequence was introduced by Newman [5], and subsequently it has been studied by many authors. For instance, Newman investigated the central limit theorem for a strictly stationary LNQD process. Zhang ... See full document
12
The consistency of estimator under fixed design regression model with NQD errors
... and asymptotic normality of regression models based on linear process ...some asymptotic properties for estimates of nonparametric regression models, and Yang et ... See full document
12
Berry Esseen bounds of weighted kernel estimator for a nonparametric regression model based on linear process errors under a LNQD sequence
... the nonparametric model ...and asymptotic normality of the estimator of g( · ), and Qin [2] obtained the strong consistency of the estimator of g( · ...the nonparametric model ... See full document
12
Uniform convergence of estimator for nonparametric regression with dependent data
... The internal estimator was first proposed by Mack and Müller ...kernel-type regression estimators, including introduced the internal estimator ...pilot estimator (.). Linton and ... See full document
12
Consistency of the Priestley–Chao estimator in nonparametric regression model with widely orthant dependent errors
... This estimator can capture the shape of the true curve better than many polynomial regression ...P–C estimator were ...the estimator with ...and asymptotic normality also with ... See full document
13
On the Asymptotic Normality of an Estimate of a Regression Functional
... a regression estimate is applied: in a standard nonparametric regres- sion design process, one considers a finite number of real-valued features X (i) , i ∈ I , and evaluates whether these suffice to ... See full document
15
The Berry Esséen bounds for kernel density estimator under dependent sample
... the asymptotic normality of a wavelet estimator of the regression ...wavelet estimator of the regression ...density estimator under a ϕ-mixing dependent ... See full document
13
The consistency for estimator of nonparametric regression model based on NOD errors
... semiparametric regression model, Ren and Chen [11] obtained the strong consistency for the least squares estimator of b and the nonparametric estimator of g(t) based on NA samples, Hu [12] ... See full document
13
Portfolio Optimization of Financial Services Stocks in the Nigerian Stock Exchange
... quantile regression estimation for time-dependent drift parameter of diffusion ...the asymptotic bias, asymptotic variance and asymptotic normality of the local estimation ...The ... See full document
9
Bahadur representations of M estimators and their applications in general linear models
... Bahadur representations for M-estimators. Wu [47] discussed strong consistency of an M-estimator in the model (1.1) for negatively associated samples. Fan [19] considered the model (1.1) with ϕ-mixing errors, and ... See full document
32
A nonparametric hypothesis test via the Bootstrap resampling
... the nonparametric kernel re- gression technique as the main element of the hypothesis testing ...the asymptotic approach, while the former develops a test adopting the bootstrap framework because of an ... See full document
24
Merits and drawbacks of variance targeting in GARCH models
... Variance targeting estimation is a technique used to alleviate the numerical difficulties en- countered in the quasi-maximum likelihood (QML) estimation of GARCH models. It relies on a reparameterization of the model and ... See full document
25
Asymptotic normality of conditional integrals of diffusion processes
... we predict fA Z(t)dt using the conditional expectation of the integral of the diffusion process, the optimal predictor in terms of minimizing the mean squared error, given the.. observed[r] ... See full document
32
Variational methods for geometric statistical inference
... For a finite number of observations we allow a soft classification however the scaling is chosen such that in the data rich limit classifiers are binary valued. The motivation for our approach is to validate ... See full document
150
Local Polynomial Regression Estimator of the Finite Population Total under Stratified Random Sampling: A Model Based Approach
... considered nonparametric models for ξ within a model-assisted approach and obtained a local polynomial regression estimator as a generalization of the ordinary generalized regression ... See full document
13
Influence Analysis Ratios Roa, Roe, And Ldr Adequecy Against Increased Capital Ratios At Bank Syariah Mandiri
... independent variables, showed there were three independent variables included in the model that explains the variation of the variable CAR of 92.0%, while the remaining 8.0% is explained by causes other than the ... See full document
5
Strong convergence bounds of the Hill type estimator under second order regularly varying conditions
... Pickands estimator such as consistency, asymptotic normality and the strong convergence rate have been discussed by Dekkers and De Haan [5], De Haan [1] and Pan ...the asymptotic ... See full document
7
On the asymptotic normality of fourier flexible form estimates
... The bound on relative bias that we derive is stated in terms of the error in a Fourier flexible form approximation to a log cost function:. Truncation error[r] ... See full document
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